1. New liquidity measurements – LCR&NSFR Zagreb, 11.05.2012

2. Definition: Liquidity- ability to meet obligations when they come due without incurring unacceptable losses 2

3. − Liquidity was major problem during this crises for many credit institutions − The result is that most banks now try to forecast their liquidity requirements − Banking regulators also view liquidity as a major concern and that is why this two measurements were developed − Quantitative measures of liquidity are being implemented, liquidity isn’t any more only a gut feeling 3

4. LCR Liquidity coverage ratio - designed to ensure that financial institutions have the necessary assets on hand to ride out short-term liquidity disruptions. 4

5. NSFR Net stable funding ratio - requires a minimum amount of funding that is expected to be stable over a one year time horizon based on liquidity risk factors assigned to assets and off-balance sheet liquidity exposures. 5

6. Characteristics of both report − assumptions set by regulators to model stress scenario − differ base on product type and other characteristics − assumptions for deposits also differ based on counterparty (whether it is retail, corporate…) and whether we have operational relationship with a client 6

7. Deposits: 7 − Whether client has relationship/operational relationship with bank or not has big influence on run-off rate. − Currently definition of relationship is left on credit institutions to define and presence of relationship should reflect stability of the deposit – gives us opportunity to develop our own models.

8. Deposits: Relationship → stable deposit; withdrawal of these deposits should be highly unlikely -most important issue in new LQ measurements 8

9. Deposits: 9 Deposits Retail deposits Other deposits Model done based on historical data Retail-with operation relationship Retail-no operation relationship Corp-with operation relationship Corp-no operation relationship Corp deposits

10. Model: 10 Existing loan Incomes Trans. accounts Monthly turnovers in last 12 months..... Predictor variables (X) : Respons variable (Y) : Stability flag: 1-stable 0-less stable Binary − Response variable has only two possible outcomes so we will use logistic regression as a model.

11. Model: 11 − log[P{y=1}/(1-P{y=1})] is called logistic transformation or logit − It transforms binary variable into continuous one: − Note: response variable is no longer y but P{y=1}

12. Model: − Formula that expresses the probability of success directly: 12

13. − Main concern is to make good model to differential stable and less stable deposits in retail. This model will have influence on the entire bank (e.g. pricing of deposits, liquidity ratios, interest rates for clients, regulatory requirements…) 13

14. Modeling amount of deposits: − One more very important issue is to forecast volume of the deposits 14

15. Thanks for the attention!